Platforms, systems, media, and methods for high-utilization product expert logistics

Abstract

Described are platforms, systems, media, and methods for maintaining a database of items associated with one or more skill requirements and a visit duration; maintaining a database of experts associated with one or more skill proficiencies, a location, and a schedule; receiving a request from a consumer for delivery by an expert of one or more items in the database to a consumer address; identifying experts in the database having skill proficiencies matching the skill requirements of the one or more items and available in a timeslot for the visit duration of the one or more items; presenting timeslots for which one or more experts are identified to the consumer and allowing the consumer to select a timeslot; and selecting an expert from among the identified experts in the selected timeslot based on shortest travel time; provided that utilization of the selected expert exceeds a predetermined utilization threshold.

What is claimed is: 
     
         1 . A multimodal logistics platform configured to schedule a plurality of real-time orders and future orders within a set processing period, the platform comprising:
 a) a consumer processor configured to provide a consumer application comprising a software module presenting an interface allowing a consumer to browse a plurality of items and submit one or more of the plurality of real-time orders and future orders, each real-time order and future order comprising one or more of the plurality of items for delivery by one or more experts, a consumer address, and a delivery timeslot; and   b) a server processor configured to provide a server application comprising:
 (i) a database of items, each item associated with a skill requirement, a visit duration, a burden score, a time-of-day requirement, and a SKU; 
 (ii) a database of experts, each expert associated with a skill proficiency, and a current expert schedule; and 
 (iii) a database of one or more mobile inventory units (MIUs), each MIU associated with a stock of items, a current MIU position, and a current MIU schedule; and 
   c) a scheduling parallel processor configured to provide:
 (i) a server real-time mode application comprising:
 A) a communication module receiving at least the items, the experts, and the MIUs from the server processor, and receiving the real-time orders from the consumer processor; 
 B) a combinator module applying a first operational rule in parallel to the real-time orders, the experts, and the one or more MIUs, based on the real-time order, to determine, a plurality of real-time delivery schemes that adhere to the first operational rule; 
 C) a filter module applying a second operational rule to the plurality of real-time delivery schemes, to select two or more filtered real-time delivery schemes that adhere to the second operational rule; and 
 D) a scorer module applying a third operational rule to the filtered real-time delivery schemes, to score the filtered real-time delivery schemes and determine which filtered real-time delivery scheme has the best score; and 
 
 (ii) a server future mode application comprising:
 A) a communication module receiving at least the items, the experts, and the MIUs from the server processor, and receiving a future order from the consumer processor; 
 B) a zoning module assigning a zone to each future order based on the consumer address; and 
 C) an availabilities module determining in parallel a plurality of future delivery schemes for each of a plurality of future days based on the zone, the experts, the MIUs, or any combination thereof, wherein the future delivery scheme comprises a plurality of the future orders within a delivery window on one of the future days; 
 
   provided that the logistics platform is configured to meet or exceed a predetermined utilization threshold.   
     
     
         2 . The platform of  claim 1 , wherein the predetermined utilization threshold is measured in dollars of revenue/expert/minute, and wherein the predetermined utilization threshold is at least 2 dollars of revenue/expert/minute. 
     
     
         3 . The platform of  claim 1 , wherein the predetermined utilization threshold is measured as a percentage of a shift of the expert that is spent with a customer, and wherein the predetermined utilization threshold is at least 40%. 
     
     
         4 . The platform of  claim 1 , wherein the server application further comprises a database of warehouses, each warehouse associated with a warehouse inventory and a warehouse location, and wherein the combinator module further applies the first operational rule to the warehouses. 
     
     
         5 . The platform of  claim 1 , wherein the server application is configured to allow an administrator to modify at least one of the first operational rule, the second operational rule, and the third operational rule in real-time, and without reprograming the server application. 
     
     
         6 . The platform of  claim 1 , wherein at least one of the server processor, the scheduling processor, and the consumer processor comprises a parallel processor. 
     
     
         7 . The platform of  claim 6 , wherein the parallel processor comprises a distributed computing parallel processor. 
     
     
         8 . The platform of  claim 1 , wherein the server application further comprises a notification module communicating a delivery update to the consumer. 
     
     
         9 . The platform of  claim 1 , wherein the server application further comprises a scheduling module modifying the current expert schedule of at least one of the experts in real-time to include the filtered delivery scheme with the best score. 
     
     
         10 . The platform of  claim 1 , wherein at least one of the combinator module applying the first operational rule, the filter module applying a second operational rule, and the scorer module applying the third operational rule occurs continuously. 
     
     
         11 . The platform of  claim 1 , wherein at least one of the combinator module applying the first operational rule, the filter module applying a second operational rule, and the scorer module applying the third operational rule occurs periodically. 
     
     
         12 . The platform of  claim 1 , wherein at least one of the combinator module applying the first operational rule, the filter module applying a second operational rule, and the scorer module applying the third operational rule occurs after a set number of real-time orders. 
     
     
         13 . The platform of  claim 1 , wherein the server application is capable of updating at least one of the database of items, the database of experts and the database of one or more MIUs in real-time. 
     
     
         14 . The platform of  claim 1 , wherein the server application further comprises an MIU stocking module applying a fourth operational rule to at least one of the database of items, the database of experts, and the database of one or more MIUs, to determine an MIU inventory for one or more of the MIUs that adheres to the fourth operational rule. 
     
     
         15 . The platform of  claim 1 , wherein a set real-time prioritization percentage of the current expert schedule is dedicated to real-time orders. 
     
     
         16 . The platform of  claim 1 , wherein the communication module of the server real-time mode application receives at least the items, the experts and the MIUs from the server processor before the combinator module applies the first operational rule. 
     
     
         17 . The platform of  claim 1 , wherein scheduling parallel processor is further configured to provide a backorder application comprising:
 a) a communication module receiving at least the items, the experts, and the MIUs from the server processor, and receiving an out-of-stock order from the consumer processor, wherein the item comprises an out-of-stock item or an unreleased item;   b) a zoning module assigning a zone to each out-of-stock order based on the consumer address; and   c) an availabilities module determining in parallel a plurality of backorder delivery schemes for each of a plurality of future days based on the zone, the experts, the MIUs, or any combination thereof, wherein the backorder delivery scheme comprises a plurality of the out-of-stock orders within a delivery window on one of the future days.   
     
     
         18 . A multimodal logistics computer-implemented system comprising: a digital processing device comprising: at least one processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to create a high-utilization product expert logistics application comprising:
 a) a consumer module receiving a real-time order and a future order submitted by a consumer, the real-time order and the future order comprising one or more items selected from a plurality of items for delivery by one or more experts, a consumer address, and a delivery timeslot;   b) a database of items, each item associated with a skill requirement, a visit duration, a burden score, a time-of-day requirement, and a SKU;   c) a database of experts, each expert associated with a skill proficiency and a current expert schedule;   d) a database of one or more mobile inventory units (MIUs), each MIU associated with a stock of items, a current MIU position, and a current MIU schedule;   e) a real-time parallel scheduling module:
 (i) applying a first operational rule to the real-time order, the experts, and the one or more MIUs, to determine a plurality of possible delivery schemes that adhere to the first operational rule; 
 (ii) applying a second operational rule to the plurality of possible delivery schemes, to select two or more filtered delivery schemes that adhere to the second operational rule; and 
 (iii) applying a third operational rule to the filtered delivery schemes, to score the filtered delivery schemes and determine which filtered delivery scheme has the best score; and 
   f) a future parallel scheduling module:
 (i) assigning a zone to each future order based on the consumer address; and 
 (ii) determining in parallel a plurality of future delivery schemes for each of a plurality of future days based on the zone, the experts, the MIUs, or any combination thereof, wherein the future delivery scheme comprises a plurality of the orders within a delivery window on one of the future days; 
   provided that the application configured to, on average, meet or exceed a predetermined utilization threshold.   
     
     
         19 . The system of  claim 18 , wherein the predetermined utilization threshold is measured as dollars of revenue/expert/minute, and wherein the predetermined utilization threshold is at least 2 dollars of revenue/expert/minute. 
     
     
         20 . The system of  claim 18 , wherein the predetermined utilization threshold is measured as a percentage of a shift of the expert that is spent with a customer, and wherein the predetermined utilization threshold is at least 40%. 
     
     
         21 . The system of  claim 18 , wherein at least one of the combinator module applying the first operational rule, the filter module applying a second operational rule, and the scorer module applying the third operational rule occurs after a set number of orders. 
     
     
         22 . The system of  claim 18 , wherein the server application is capable of updating at least one of the database of items, the database of experts and the database of MIUS in real-time. 
     
     
         23 . The system of  claim 18 , wherein the server application further comprises an MIU stocking module applying a fourth operational rule to at least one of the database of items, the database of experts, and the database of one or more MIUs, to determine an MIU inventory for one or more of the MIUs that adheres to the fourth operational rule. 
     
     
         24 . The system of  claim 18 , wherein a set real-time prioritization percentage of the current expert schedule is dedicated to real-time orders. 
     
     
         25 . The system of  claim 18 , wherein scheduling parallel processor is further configured to provide a backorder application comprising:
 a) a communication module receiving at least the items, the experts, and the MIUs from the server processor, and receiving the future orders from the consumer processor, wherein the item comprises an out-of-stock item or an unreleased item;   b) a zoning module, assigning a zone to each future order based on the consumer address; and   c) an availabilities module determining in parallel a plurality of backorder delivery schemes for each of a plurality of future days based on the zone, the experts, the MIUs, or any combination thereof, wherein the backorder delivery scheme comprises a plurality of the out-of-stock orders within a delivery window on one of the future days.   
     
     
         26 . A parallel computer-implemented multimodal method of providing high-utilization logistics for a scheduling a plurality of real-time orders and future orders within a set processing period, the method comprising:
 a) maintaining, in a computer storage, a database of items, each item associated with a skill requirement, a visit duration, a burden score, a time-of-day requirement, and a SKU;   b) maintaining, in the computer storage, a database of experts, each expert associated with a skill proficiency and a current expert schedule;   c) maintaining, in the computer storage, a database of one or more mobile inventory units (MIUs), each MIU associated with a stock of items, a current MIU position, and a current MIU schedule;   d) receiving, by a computer, a real-time order and a future order from a consumer for delivery of one or more of the items to a consumer address, during a delivery timeslot, the delivery by at least one of the experts;   e) performing a parallel real-time mode comprising:
 (i) determining, by the computer, one or more possible delivery schemes by applying a first operational rule to the real-time order, the experts, and the one or more MIUs; 
 (ii) filtering, by the computer, the one or more possible delivery schemes to select one or more filtered delivery schemes, wherein the filtering comprises applying a second operational rule to the possible delivery schemes; and 
 (iii) scoring, by the computer, the filtered delivery schemes by applying a third operational rule to the filtered delivery schemes and determining which filtered delivery scheme has the best score; and 
   f) performing a parallel future mode comprising:
 (i) assigning, by the computer, a zone to each future order based on the consumer address; and 
 (ii) determining in parallel, by the computer, a plurality of future delivery schemes for each of a plurality of future days based on the zone, the experts, the MIUs, or any combination thereof, wherein the future delivery scheme comprises a plurality of the orders within a delivery window on one of the future days; 
   wherein the filtered delivery scheme with the best score or the future delivery scheme meets or exceeds a predetermined utilization threshold.   
     
     
         27 . The method of  claim 26 , wherein the predetermined utilization threshold is measured as dollars of revenue/expert/minute, and wherein the predetermined utilization threshold is at least 2 dollars of revenue/expert/minute. 
     
     
         28 . The method of  claim 26 , wherein the predetermined utilization threshold is measured as a percentage of a shift of the expert that is spent with a customer, and wherein the predetermined utilization threshold is at least 40%. 
     
     
         29 . The method of  claim 26 , further comprising communicating, by a dispatch module, the filtered delivery scheme with the best score to an administrator. 
     
     
         30 . The method of  claim 26 , further comprising performing a backorder mode comprising:
 a) receiving, by the computer, at least the items, the experts, and the MIUs from the server processor, and receiving the future orders from the consumer processor, wherein the item comprises an out-of-stock item or an unreleased item;   b) assigning, by the computer, a zone to each future order based on the consumer address; and   c) determining, in parallel, by the computer, a plurality of backorder delivery schemes for each of a plurality of future days based on the zone, the experts, the MIUs, or any combination thereof, wherein the backorder delivery scheme comprises a plurality of the out-of-stock orders within a delivery window on one of the future days.